Details

Title

Neural Network Prediction Model –Applied to U.S. Industrial Greenhouse Gas Emissions

Journal title

Archives of Environmental Protection

Yearbook

2025

Volume

51

Issue

1

Authors

Affiliation

Tseng, Shih-Hsien : National Taiwan University of Science and Technology,Taiwan ; Wang, Chia-Hsuan : National Taiwan University of Science and Technology,Taiwan ; Duong, Thi Ha Trang : National Taiwan University of Science and Technology,Taiwan

Keywords

deep learning ; greenhouse gas emission ; GRU ; RNN ; transformer ; time series prediction model

Divisions of PAS

Nauki Techniczne

Coverage

103-115

Publisher

Polish Academy of Sciences

Bibliography

  1. Alibrahim, H. & Ludwig, S. A. (2021, 28 June-1 July 2021). Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization. 2021 IEEE Congress on Evolutionary Computation (CEC),
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  4. Fang, Z., Yang, H., Li, C., Cheng, L., Zhao, M. & Xie, C. (2021). Prediction of PM2. 5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR. Archives of Environmental Protection, 47(3).
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  7. Hyndman, R. J. & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
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  13. Sen, P., Roy, M. & Pal, P. (2016). Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. Energy, 116, pp. 1031-1038.
  14. Sonata, I. & Heryadi, Y. (2024, 17-18 July 2024). Comparison of LSTM and Transformer for Time Series Data Forecasting. 2024 7th International Conference on Informatics and Computational Sciences (ICICoS),
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  16. Szeląg, B., Bartkiewicz, L., Studziński, J. & Barbusinski, K. (2017). Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear. Archives of Environmental Protection, 43. DOI:10.1515/aep-2017-0030
  17. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
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Date

13.02.2025

Type

Article

Identifier

DOI: 10.24425/aep.2025.153754

DOI

10.24425/aep.2025.153754

Abstracting & Indexing

Abstracting & Indexing


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